ArticleProtein and peptide letters2024
The Features of Shared Genes among Transcriptomes Probed in Atopic Dermatitis, Psoriasis, and Inflammatory Acne: S100A9 Selection as the Target Gene.
Article in Protein and peptide letters, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- JAK-STAT pathway-associated skin diseases: a refined functional framework for inflammatory skin diseases.Frontiers in immunology · 2026Review
- AR and ITGAL: Key Mediators of Andrographis paniculata's Anti-Psoriatic Effects Revealed by Multi-Omics Analysis.Combinatorial chemistry & high throughput screening · 2026Article
- Computational prioritization of candidate TCDD-associated targets in retinoblastoma using network toxicology and transcriptomic analysis.Frontiers in genetics · 2026Article
- Exploring the Causal Relationship and Molecular Mechanisms Between Fasting Insulin and Androgenetic Alopecia: A Mendelian Randomization Study with Bioinformatics Analysis.Clinical, cosmetic and investigational dermatology · 2025Article
- Portfolio analysis of single-cell RNA-sequencing and transcriptomic data unravels immune cells and telomere-related biomarkers in sepsis.Frontiers in immunology · 2025Article
- Potential Targets and Mechanisms of Saikosaponin D in Psoriasis: A Bioinformatic and Experimental Study on Oxidative Stress.Journal of inflammation research · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAtopic dermatitis (AD), psoriasis (PS), and inflammatory acne (IA) are well-known as inflammatory skin diseases. Studies of the transcriptome with altered expression levels have reported a large number of dysregulated genes and gene clusters, particularly those involved in inflammatory skin diseases.
objectiveTo identify genes commonly shared in AD, PS, and IA that are potential therapeutic targets, we have identified consistently dysregulated genes and disease modules that overlap with AD, PS, and IA.
methodsMicroarray data from AD, PS, and IA patients were downloaded from Gene Expression Omnibus (GEO), and identification of differentially expressed genes from microarrays of AD, PS, and IA was conducted. Subsequently, gene ontology and gene set enrichment analysis, detection of disease modules with known disease-associated genes, construction of the protein-protein interaction (PPI) network, and PPI sub-mapping analysis of shared genes were performed. Finally, the computational docking simulations between the selected target gene and inhibitors were conducted.
resultsWe identified 50 shared genes (36 up-regulated and 14 down-regulated) and disease modules for each disease. Among the shared genes, 20 common genes in PPI network were detected such as
conclusionOverall, our approach may become an effective strategy for discovering new disease candidate genes for inflammatory skin diseases with a reevaluation of clinical data.
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